Abstract: With the rise of big data and complex modeling, probabilistic inference (i.e. computing probability of an event given some observation) has emerged as a key problem. The current state of art techniques are either exact and face scalability issues or provide very weak approximation guarantees. We introduce a new computational paradigm, which makes only few feasibility queries on models augmented with random constraints, that provides (ϵ-δ) guarantees and scalable practical algorithms.
Our new approach builds upon prior work in computational theory and techniques based on universal hashing. I will discuss how we can further use these techniques to generate samples from complex distributions which has been key to constrained-random verification over last two decades.

Speaker Profile: Kuldeep Meel is a PhD student in Rice working with Prof. Moshe Vardi and Prof. Supratik Chakraborty. His research broadly falls into the intersection of probabilistic reasoning, computer-aided verification and formal methods. He is the recipient of 2013-14 Andrew Ladd Fellowship. He received his M.S. from Rice in 2014 and B.Tech. from IIT Bombay in 2012.